# A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends

arXiv: https://arxiv.org/abs/2409.19629 · HTML: https://mlsanity.com/paper/2409.19629/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 115 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Builds on (most cited first, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2103.07719/) (2021)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://mlsanity.com/paper/2403.14735/) (2024)
- [Graph Foundation Models: Concepts, Opportunities and Challenges](https://mlsanity.com/paper/2310.11829/) (2023)
- [Edge Contraction Pooling for Graph Neural Networks](https://mlsanity.com/paper/1905.10990/) (2019)
- [Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://mlsanity.com/paper/2309.05305/) (2023)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [Graph-Aware Contrasting for Multivariate Time-Series Classification](https://mlsanity.com/paper/2309.05202/) (2023)
- [Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery](https://mlsanity.com/paper/1810.05644/) (2018)
- [Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning](https://mlsanity.com/paper/2403.03645/) (2024)
- [Handling Variable-Dimensional Time Series with Graph Neural Networks](https://mlsanity.com/paper/2007.00411/) (2020)
- [Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach](https://mlsanity.com/paper/2109.12111/) (2021)

## Built on by (most cited first, 3 of 3)

- [Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation](https://mlsanity.com/paper/2505.21525/) (2025)
- [Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks](https://mlsanity.com/paper/2511.02957/) (2025)
- [Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights](https://mlsanity.com/paper/2507.09766/) (2025)

## Abstract (arXiv)

Remaining Useful Life (RUL) prediction is a critical aspect of Prognostics and Health Management (PHM), aimed at predicting the future state of a system to enable timely maintenance and prevent unexpected failures. While existing deep learning methods have shown promise, they often struggle to fully leverage the spatial information inherent in complex systems, limiting their effectiveness in RUL prediction. To address this challenge, recent research has explored the use of Graph Neural Networks (GNNs) to model spatial information for more accurate RUL prediction. This paper presents a comprehensive review of GNN techniques applied to RUL prediction, summarizing existing methods and offering guidance for future research. We first propose a novel taxonomy based on the stages of adapting GNNs to RUL prediction, systematically categorizing approaches into four key stages: graph construction, graph modeling, graph information processing, and graph readout. By organizing the field in this way, we highlight the unique challenges and considerations at each stage of the GNN pipeline. Additionally, we conduct a thorough evaluation of various state-of-the-art (SOTA) GNN methods, ensuring consistent experimental settings for fair comparisons. This rigorous analysis yields valuable insights into the strengths and weaknesses of different approaches, serving as an experimental guide for researchers and practitioners working in this area. Finally, we identify and discuss several promising research directions that could further advance the field, emphasizing the potential for GNNs to revolutionize RUL prediction and enhance the effectiveness of PHM strategies. The benchmarking codes are available in GitHub: https://github.com/Frank-Wang-oss/GNNRULBenchmarking.